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  1. Stackups
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  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs Dask

Airflow vs Dask

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Dask
Dask
Stacks116
Followers142
Votes0

Airflow vs Dask: What are the differences?

Introduction

Airflow and Dask are both popular tools in the data engineering and data processing domains. While they have some similarities, there are key differences that set them apart. In this article, we will explore six key differences between Airflow and Dask.

  1. Data Processing vs Workflow Orchestration: Airflow is primarily a workflow orchestration tool that allows you to define, schedule, and monitor complex workflows. It provides a way to create Directed Acyclic Graphs (DAGs) for data pipelines, where tasks are executed based on their dependencies and schedules. On the other hand, Dask is a parallel computing library that provides dynamic task scheduling and parallel execution of computations, enabling scalable data processing and analysis.

  2. Language Support: Airflow is built with Python and offers extensive support for Python-based workflows. It provides a Pythonic way of defining tasks and workflows using Python code. Dask, on the other hand, supports Python, but it also offers support for other languages like R and Scala. This makes Dask more versatile in multi-language data processing scenarios.

  3. Scaling and Deployment: Airflow is designed for horizontal scaling and is commonly deployed in a distributed setup using a cluster of Airflow workers. It can handle large-scale workflows and distribute tasks across multiple workers for parallel execution. Dask, on the other hand, allows for both horizontal and vertical scaling. It leverages technologies like Apache Mesos, Kubernetes, or YARN to distribute work across a cluster of machines or scale up resources on a single machine.

  4. Task-Level vs Computational Graph Parallelism: Airflow executes tasks in a sequential manner, where each task depends on the successful completion of its upstream tasks. This task-level parallelism ensures that the workflows are executed in a controlled manner with dependencies in mind. Dask, on the other hand, uses computational graph parallelism to execute computations. It creates a dynamic task graph based on the operations performed and optimizes the execution by parallelizing the data processing steps.

  5. Built-in vs External Task Executors: Airflow comes with built-in executors like LocalExecutor and CeleryExecutor, which handle the execution of tasks on the worker machines. These built-in executors provide options for distributed task execution. Dask, on the other hand, acts as a task scheduler and relies on external compute engines like Dask.distributed or Dask-Yarn to execute the tasks. This allows Dask to leverage the capabilities of different compute engines based on the deployment environment.

  6. Community and Ecosystem: Airflow has a large and active community with a wide range of integrations and plugins available. It has been widely adopted by organizations and has a mature ecosystem with support for various databases, cloud providers, and third-party tools. Dask also has a growing community and ecosystem, but it is relatively newer compared to Airflow. However, Dask's integration with the PyData ecosystem and its ability to work seamlessly with popular tools like Pandas, NumPy, and Scikit-learn make it a valuable addition to the data processing landscape.

In summary, Airflow focuses on workflow orchestration, provides extensive Python support, and allows for horizontal scaling with built-in task executors. On the other hand, Dask emphasizes parallel computation, supports multiple languages, enables both horizontal and vertical scaling, and relies on external task executors for task execution.

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Advice on Airflow, Dask

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Airflow
Airflow
Dask
Dask

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Supports a variety of workloads;Dynamic task scheduling ;Trivial to set up and run on a laptop in a single process;Runs resiliently on clusters with 1000s of cores
Statistics
Stacks
1.7K
Stacks
116
Followers
2.8K
Followers
142
Votes
128
Votes
0
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
No community feedback yet
Integrations
No integrations available
Pandas
Pandas
Python
Python
NumPy
NumPy
PySpark
PySpark

What are some alternatives to Airflow, Dask?

GitHub Actions

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It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

NumPy

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

Luigi

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

PyXLL

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

Unito

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Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

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na

PromptX

PromptX

PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.

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